An artificial immune system was modelled with self/non-self selection to overcome abnormity in a mobile robot demo. The immune modelling includes the innate immune modelling and the adaptive immune modelling. The self...An artificial immune system was modelled with self/non-self selection to overcome abnormity in a mobile robot demo. The immune modelling includes the innate immune modelling and the adaptive immune modelling. The self/non-self selection includes detection and recognition, and the self/non-self detection is based on the normal model of the demo. After the detection, the non-self recognition is based on learning unknown non-self for the adaptive immunization. The learning was designed on the neural network or on the learning mechanism from examples. The last step is elimination of all the non-self and failover of the demo. The immunization of the mobile robot demo is programmed with Java to test effectiveness of the approach. Some worms infected the mobile robot demo, and caused the abnormity. The results of the immunization simulations show that the immune program can detect 100% worms, recognize all known Worms and most unknown worms, and eliminate the worms. Moreover, the damaged files of the mobile robot demo can all be repaired through the normal model and immunization. Therefore, the immune modelling of the mobile robot demo is effective and programmable in some anti-worms and abnormity detection applications.展开更多
基金the National Natural Science Foundation of China under Grant Nos.69903006,60373065,60721002(国家自然科学基金)the National High-Tech Research and Development Plan of China under Grant No.2007AA01Z334(国家高技术研究发展计划(863))the Program for New Century Excellent Talents in University of China under Grant No.NCET-04-0460(新世纪优秀人才资助计划)
基金Projects(60234030, 60404021) supported by the National Natural Science Foundation of China project(040125) supported by the Doctoral Research Grant of Central South University
文摘An artificial immune system was modelled with self/non-self selection to overcome abnormity in a mobile robot demo. The immune modelling includes the innate immune modelling and the adaptive immune modelling. The self/non-self selection includes detection and recognition, and the self/non-self detection is based on the normal model of the demo. After the detection, the non-self recognition is based on learning unknown non-self for the adaptive immunization. The learning was designed on the neural network or on the learning mechanism from examples. The last step is elimination of all the non-self and failover of the demo. The immunization of the mobile robot demo is programmed with Java to test effectiveness of the approach. Some worms infected the mobile robot demo, and caused the abnormity. The results of the immunization simulations show that the immune program can detect 100% worms, recognize all known Worms and most unknown worms, and eliminate the worms. Moreover, the damaged files of the mobile robot demo can all be repaired through the normal model and immunization. Therefore, the immune modelling of the mobile robot demo is effective and programmable in some anti-worms and abnormity detection applications.
